The invention pertains to the field of manufacturing methods. More particularly, the invention pertains to methods of ordering and managing part and component inventories for services or manufacturing.
The problem of determining the amount of components and subassemblies that are to be carried in inventory in a factory to meet customer demand has a long history. The complexity of the problem in practice depends on, among other things, the nature of customer demand, supplier lead times, and product structure involved. In most cases, customer demand and supplier lead times are probabilistic. The product structure may require a constant number of units of a list of components per unit of the finished product (xe2x80x9cfixed bill-of-materialsxe2x80x9d) or depend on the customer order (xe2x80x9cvariable bill-of-materialsxe2x80x9d). The latter is the case in produce-to-order (engineer-to-order, assembly-to-order) type systems.
The complexity of the material management problem in the production of products with fixed and variable bills-of-materials (BOM) is significantly different. When a product consists of a known number of units of a fixed list of components (fixed bill-of-materials), there are well established techniques such as the traditional inventory control techniques, Material Requirements Planning (MRP), and their extensions (Nahmias, 1993) to manage component, subassembly, and other material inventories. When a product is built according to a customer order (such as Lucent""s 5ESS switch), the set (list) of components/subassemblies as well as the number of units of each component in the set needed for the product may change from one order to another, making the material management problem extremely complex.
The traditional inventory control techniques, MRP system, and their extensions were originally developed for products with fixed bill-of-materials. They are primarily used for material ordering and management. MRP, for instance, takes either demand forecasts for a finished product or scheduled production quantities for it from a production plan, and determines the numbers of components and subassemblies to be ordered/produced by multiplying the required production quantities by the fixed numbers of units of components and subassemblies specified in the product""s fixed BOM. To determine when they should be ordered/produced, it uses the supplier and manufacturing lead times. Some factories also try to use this approach to manage materials for complex products with variable BOM""s by first defining building blocks (large subassemblies) that go in to a customer order. The number of units of different building blocks required by an order may vary from order to order as well as blocks"" contents (i.e. components and subassemblies in each). With this approach, customer demand is forecasted at building block level. It is then converted in to component level requirements assuming that each building block has a fixed bill-of-material. Composition of a xe2x80x9ctypicalxe2x80x9d is obtained from the variable BOM by simply setting the number of units of a component needed per building block equal to a fixed number in order to cover most orders. Hence, this modified MRP approach would not result in effective management of materials.
None of the existing material management systems links material ordering and inventory control policies with the desired customer service levels in manufacturing products with variable BOM. For instance, the whole order delivery (WOD) performance (or its variants) is one of the critical customer service metrics tracked by factories operating in a manufacture-to-order environment. It is defined as the percentage of customer orders completed within the time interval promised to customers over a period of time (e.g. a week, a month). It is considered to be an indicator of customer satisfaction in terms of timeliness of order completion. As known, material availability is one of the most critical determinants of WOD performance. Without needed materials, an order can not be started, resulting in delays and late completion of it. Specifically, the percentage of the times an arriving order facing no material shortages (referred to as xe2x80x98the order fill ratexe2x80x99 in what follows) has a direct relationship with the WOD performance of such a factory. Instead of determining the amount of components, subassemblies, and other materials needed to be ordered and carried in inventory to reach the desired order level fill rate, all existing material management systems determine these quantities for each component/subassembly separately (without taking the order composition and order to order variations), and hope that, if they are high enough, the desired order fill rate will be reached. Thus, these approaches fail to link the material requirements with the performance metrics in a manufacture-to-order environment. The order based material management system described here addresses this problem and links, for the first time, the customer service objectives such as the order fill rate and delivery interval targets with the material requirements in a produce-to-order environment.
The material management technique of the invention is for managing materials in factories producing highly customized products. It may be used for planning and ordering components/subassemblies and other types of material, timing of these orders, and determining appropriate inventory levels to reach the desired order fill rate targets at factories producing complex products with variable bills-of-materials.
The Order Based Materials Management (OBMM) method of the invention uses forecasts of actual customer orders (not building block or component level demand forecasts) to determine component stocking levels. There may be several types of orders (an order type may be a particular model with different options or size that can form a complete customer order) and customer demand forecasts for them. Each order type is specified by a variable BOM called an xe2x80x9corder profilexe2x80x9d. An order profile is represented by a multivariate normal probability distribution (a joint normal probability distribution of two or more variables) with each random variable representing the uncertain number of units of a component or subassembly that may be needed to build a customer order of that type. Thus, the numbers of units of different components/subassemblies needed to build a certain type of order are uncertain (i.e. a variable BOM). The profile of an order type is a joint multivariate distribution of all components/subassemblies (or so called xe2x80x9cType Axe2x80x9d items in material management literature) that may be needed for an order of that type. Thus, it also captures the correlation between the numbers of units of different components that may be needed for orders of a particular type.
Given order level forecasts and order profiles for different order types, the method of the invention uses a desired xe2x80x9corder fill ratexe2x80x9d (that is, on average, the percentage of the times all components needed for an order will be available in inventory when the order arrives) to determine the needed component/subassembly buffer levels. As explained before, the order fill rate is one of the important factors affecting the Whole Order Delivery (WOD) performance. The time characteristics of final assembly and test operations, and delay time distribution (expedition) when there are material shortages are among the other factors affecting the WOD performance.
Using i) the desired order fill rate, ii) order profiles, iii) component lead times, iv) forecasts, and v) forecast error variances, OBMM next determines the target number of orders over the lead times plus one review period for which to maintain materials. If materials for this target number of orders are planned for, and ordered and arrive on time, the average order fill rate over time will be the target order fill rate value.
To determine component level orders and inventory quantities to achieve the desired order fill rate for each order type, this target number and order profile are then combined to obtain a joint multivariate normal distribution of components that may be needed for the target number of orders. The joint distributions for different order types are combined using the statistical properties of these distributions. This allows the invention to take advantage of the component commonalties between order types. Order-up-to periodic inventory policies, also known as the (S, R) system (Silver and Peterson, 1998) for components and subassemblies are then developed from this combined multivariate distribution.
In summary, the method of the invention starts with the desired order fill rate. Using the desired order fill rate, order level forecasts, and forecast error variances, it first obtains target numbers for different order types. Combining the target numbers with the associated order profiles, an overall multivariate component demand distribution is obtained. And finally, from that multivariate distribution, it determines order-up-levels for components to achieve the desired order fill rate.